The first class is multi-module multi-step, which uses different modules and interrelated processing steps to extract entities and relations serially.
For example,
a line of works first identify all entities in a sentence, and then perform relation classification between every entity pairs [1]}, [2]}, [3]}, [4]}.
The second line of works first detect the relations expressed by a sentence rather than preserve all redundant relations; then head entities and tail entities are predicted [5]}, [6]}, [7]}, [8]}.
The third line of works first distinguish all head entities, and then inference corresponding relations and tail entities via sequence labeling or question answering [9]}, [10]}, [11]}, [12]}, [13]}.
Despite their success, the multi-module multi-step methods suffer from the problem of cascading errors, as the mistakes in early steps cannot be corrected in later steps.
